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About 10,000 papers for “Process variation”

Process Variation

Rabab Ezz-Eldin, Magdy A. El-Moursy, Hesham F. A. Hamed · 2015

Cited by 1

Process Technology Variation

Kelin J. Kuhn, M.D. Giles, D. Becher et al. · IEEE Transactions on Electron Devices · 2011

Moore's law technology scaling has improved performance by five orders of magnitude in the last four decades. As advanced technologies continue the pursuit of Moore's law, a variety of challenges will need to be overcome. One of these challenges is the management of process variation. This paper discusses the importance of process variation in modern transistor technology, reviews front-end variation sources, presents device and circuit variation measurement techniques, including circuit and memory data from the 32-nm node, and compares recent intrinsic transistor variation performance from the literature.

Cited by 363

Understanding Variation and Statistical Process Control: Process Variation and Statistical Process Control (SPC)

Johnson, Lou (process engineer) · ATE Central (National Science Foundation) · 2017

This video, published by the Nanotechnology Applications and Career Knowledge Support (NACK) Center at Pennsylvania State University, focuses on process variation and statistical process control. During the lecture, Lou Johnson from Pennsylvania State University discusses the differences between normal and special cause variation, control limits and how they differ from specification limits, and individual charts, including calculating individual charts and creating them from historical data. Building on the discussion of individuals charts, Johnson covers the application of the eight Nelson Rules for determining out-of-control signals from a control chart, creating an X bar and R chart using data subgroups, and the advantages of creating a control chart with subgroup data.The video runs 58:26 minutes in length. Presentation slides and an audio version of the webinar are also included.

Cited by 0

Understanding Variation and Statistical Process Control: Variation and Process Capability Calculations

Johnson, Lou (process engineer) · ATE Central (National Science Foundation) · 2017

This video, published by the Nanotechnology Applications and Career Knowledge Support (NACK) Center at Pennsylvania State University, focuses on variation and process capability calculations. During the video, Lou Johnson from Pennsylvania State University discusses variation and its relationship to quality in manufacturing, normal distribution as a model to understand, quantify, and visualize variation, and comparing variation to specifications. Additionally, Johnson highlights the concepts and calculations of Cp (process capability), Cpk (process capability index), and Ppk (process performance index).The video runs 46:13 minutes in length. Presentation slides and an audio version of the webinar are also included.

Cited by 0

PROCESS DEVELOPMENTS Temperature variation

2016

and its effects on pH This important parameter is often overlooked in day-to-day operations K. n. RAMAKUMAR, Indian Oil Corp. Ltd., Gujarat, India The pH of service solution in anypiece of equipment like a boileror heat exchanger is an important parameter that should be maintained in a specified range. Equipment metallurgi-cal properties must also be considered. Normally, pH measurements are made by online pH analyzers located downstream of coolers at a much lower temperature compared to actual operating condition. These readings are generally cross-checked by operating personnel with color indica-tion From time to time. But the crux of this issue is whether measuring the pH at a temperature lower than actual operating condition fulfills the actual purpose of maintaining pH. Experi-mental data accentuates the fact that pH tends to be on the acidic side at higher tem-peratures. The catastrophic effect of mate-rial deterioration or corrosion associated with such a phenomenon is also explained using vis-a-vis Pourbaix diagrams. The need to specify pH as a function of temperature is indelibly felt in this regard. Temperature. Ever wondered what a change in pH with temperature could do to the equipment material of construc-tion. ' The shift in pH associated with temperature fluctuation can lead to rapid equipment deterioration, especially with boilers and heat exchangers. Basically, pH can be defined as the negative logarithm of the hydronium ion (H3O*) concentration, where: It is measured on a scale ranging from 0 to 14. If the H3O * concentration is more than the hydroxide ion (OH~) concentra-tion, the material is acidic, that is, its pH is less than 7. pH measurement. A rough indication of pH can be obtained using pH papers or indicators, that change color as the pH level varies. These indicators have accuracy limitations and can be difficult to interpret correctly in colored or murky samples. More accurate pH measurements are obtained with a pH meter. A pH measure-ment system has three parts: 1. pH measuring electrode 2. Reference electrode 3. High-input impedance meter. The pH electrode can be thought of as a battery, with a voltage that varies with the solutions being measured. The pH measuring electrode is a hydrogen ion (H*)-sensitive glass bulb, with a mil-Temperature vs. pH for temper water

Cited by 0

Spatial Correlation, Process Variation Modeling, Non-stationary Variation

Eva Dyer, Mehrdad Majzoobi, Farinaz Koushanfar · 2012

Accurate characterization of spatial variation is essential for statistical performance analysis and modeling, post-silicon tuning, and yield analysis. Existing approaches for spatial modeling either assume that: (i) non-stationarities arise due to a smoothly varying trend component or that (ii) the process is stationary within regions associated with a predefined grid. While such assumptions may hold when profiling certain classes of variations, a number of recent modeling studies suggest that non-stationarities arise from both shifts in the process mean as well as fluctuations in the variance of the process. In order to provide a compact model for nonstationary process variations, we introduce a new hybrid spatial modeling framework that models the spatially varying random field as a union of non-overlapping rectangular regions where the process is assumed to be locally-stationary within each region. To estimate the parameters in our hybrid spatial model, we develop a host of techniques to both estimate the change-points in the random field and to find an appropriate partitioning of the chip into disjoint regions where the field is locally-stationary. We verify our models and results on measurements collected from 65nm FPGAs.

Cited by 0

Buffer Insertion Considering Process Variation

Jinjun Xiong, K. C. Tam, Lei He · Design, Automation, and Test in Europe · 2005

A comprehensive probabilistic methodology is proposed to solve the buffer insertion problem with the consideration of process variations. In contrast to a recent work, we point out, for the first time, that the correlation between the required arrival time and the downstream loading capacitance must be considered in order to solve the problem "correctly". We develop an efficient bottom-up recursive algorithm to calculate the joint probability density function that accurately captures the above correlation, and propose effective pruning rules to exclude probabilistically inferior solutions. We verify our buffer insertion using timing analysis with both device and interconnect variations, and show that compared to the conventional buffer insertion algorithm using nominal device and interconnect parameters, our new buffer insertion methodology can reduce the probability of timing violation by up to 30%.

Cited by 23

A process variation detection method

Vazgen Melikyan, Davit Mirzoyan, G. A. Petrosyan · Zenodo (CERN European Organization for Nuclear Research) · 2010

A new simple and versatile PV detection method is proposed that allows detecting MOS transistor's parameters variation. Theoretical analysis and simulation results for threshold voltage, mobility, oxide thickness prove the performance of the circuit (with the basic method). The use of only MOS transistors and the lack of reference sources allow obtaining small area and high detection accuracy.

Cited by 2Open access

Process Variation-Aware Photonic Design

Duane S. Boning, Sally I. El-Henawy, Zhengxing Zhang · 2021

We present methods for variation-aware compact models, adjoint-based particle defect analysis, and variation extraction for photonic components. These methods can expedite ensemble simulations and enable design-for-manufacturability to increase yield in photonic integrated circuit design.

Cited by 4

In-process Monitoring of Micromoulding – Assessment of Process Variation

B. R. Whiteside, M.T. Martyn, Phil Coates · International Polymer Processing · 2005

Abstract Advances in micromoulding technology are leading to complex, net-shape products having sub-milligramme masses with micro-scale surface features in a range of polymer and nanocomposite materials. For such small components subjected to the extreme stress, strain-rate and temperature gradients encountered in the micromoulding process, detailed process monitoring is desirable to highlight variations in moulding conditions and assist in creating a viable manufacturing process with acceptable quality products. This paper covers the implementation of a suite of sensors on a commercial micromoulding machine and detailed computer monitoring during processing of a polyacetal component over a range of processing conditions. The results determined that cavity pressure curve integral data provides the most sensitive factor for characterisation of a moulding process of a 0.34 mm 3 (0.49 mg) product. The repeatability of the process is directly compared with that of a 15.6 mm 3 (22.2 mg) product and shown to be inferior. DSC measurements of the whole products indicated little variation in average crystallinity of the products manufactured over a mould temperature range of 30 to 130 °C.

Cited by 24